Search alternatives:
process optimization » model optimization (Expand Search)
web optimization » b optimization (Expand Search), yet optimization (Expand Search), led optimization (Expand Search)
search process » research process (Expand Search), hierarchy process (Expand Search)
binary based » library based (Expand Search), linac based (Expand Search), binary mask (Expand Search)
process optimization » model optimization (Expand Search)
web optimization » b optimization (Expand Search), yet optimization (Expand Search), led optimization (Expand Search)
search process » research process (Expand Search), hierarchy process (Expand Search)
binary based » library based (Expand Search), linac based (Expand Search), binary mask (Expand Search)
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Classification performance after optimization.
Published 2025“…The proposed approach integrates binary feature selection and metaheuristic optimization into a unified optimization process, effectively balancing exploration and exploitation to handle complex, high-dimensional datasets. …”
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ANOVA test for optimization results.
Published 2025“…The proposed approach integrates binary feature selection and metaheuristic optimization into a unified optimization process, effectively balancing exploration and exploitation to handle complex, high-dimensional datasets. …”
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Wilcoxon test results for optimization.
Published 2025“…The proposed approach integrates binary feature selection and metaheuristic optimization into a unified optimization process, effectively balancing exploration and exploitation to handle complex, high-dimensional datasets. …”
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Wilcoxon test results for feature selection.
Published 2025“…The proposed approach integrates binary feature selection and metaheuristic optimization into a unified optimization process, effectively balancing exploration and exploitation to handle complex, high-dimensional datasets. …”
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Feature selection metrics and their definitions.
Published 2025“…The proposed approach integrates binary feature selection and metaheuristic optimization into a unified optimization process, effectively balancing exploration and exploitation to handle complex, high-dimensional datasets. …”
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Statistical summary of all models.
Published 2025“…The proposed approach integrates binary feature selection and metaheuristic optimization into a unified optimization process, effectively balancing exploration and exploitation to handle complex, high-dimensional datasets. …”
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Feature selection results.
Published 2025“…The proposed approach integrates binary feature selection and metaheuristic optimization into a unified optimization process, effectively balancing exploration and exploitation to handle complex, high-dimensional datasets. …”
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ANOVA test for feature selection.
Published 2025“…The proposed approach integrates binary feature selection and metaheuristic optimization into a unified optimization process, effectively balancing exploration and exploitation to handle complex, high-dimensional datasets. …”
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Classification performance of ML and DL models.
Published 2025“…The proposed approach integrates binary feature selection and metaheuristic optimization into a unified optimization process, effectively balancing exploration and exploitation to handle complex, high-dimensional datasets. …”
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Analysis and design of algorithms for the manufacturing process of integrated circuits
Published 2023“…</p><p>These files contain the following:</p><ul><li>Test cases of Ahn et al. (2019)</li><li>The implementation of the random algorith, the local search algorithm and the greedy algorithm (in Java). …”
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Hyperparameters of the LSTM Model.
Published 2025“…The AD-PSO-Guided WOA overcomes limitations of conventional optimization algorithms, such as premature convergence by balancing global search (exploration) and local refinement (exploitation). …”
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The AD-PSO-Guided WOA LSTM framework.
Published 2025“…The AD-PSO-Guided WOA overcomes limitations of conventional optimization algorithms, such as premature convergence by balancing global search (exploration) and local refinement (exploitation). …”
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Prediction results of individual models.
Published 2025“…The AD-PSO-Guided WOA overcomes limitations of conventional optimization algorithms, such as premature convergence by balancing global search (exploration) and local refinement (exploitation). …”
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